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IDENTIFICATION OF SPECIES-ENVIRONMENT RELATIONSHIPS IN THE HUDSON-RARITAN ESTUARY AND RELATED SUB-BASINS
Citation:
Pelletier, M C. AND E. D. Gallagher. IDENTIFICATION OF SPECIES-ENVIRONMENT RELATIONSHIPS IN THE HUDSON-RARITAN ESTUARY AND RELATED SUB-BASINS. Presented at Estuarine Research Federation 16th Biennial Conference, St. Petersburg, FL, November 4-8, 2001.
Description:
The US EP A's Regional Environmental Monitoring and Assessment Program (REMAP) conducted a study in 1993/94 to assess the effects of sediment contamination in the Hudson- Raritan area (Upper New York, Raritan Bay, Jamaica Bay, western Long Island Sound and the Bight Apex). This study examined the data using multivariate techniques. Principle Components Analysis (PCA) of the benthic data was used to visually identify station clusters and the dominant species associated with those sites. Redundancy analysis (RDA) was used to explore the relationship between the benthic abundances at various stations and different environmental factors. The first four PCA axes accounted for 42.4% of the variance in the benthic abundance data which effectively separated the different sub-basins of the Hudson- Raritan area. The less-impacted sites were associated with pollution-sensitive species such as Polygordius sp. while the more impacted sites were associated with opportunists such as Streblospio benedicti and oligochaetes. The. first four RDA axes accounted for only 23.1 % of the variance in the benthic abundance data although 79.4% of the species-environment relation was explained. Almost half of the explained variation (10%) was due to the temperature + depth factor. Approximately 25 % of the explained variation (4.5%) was due to the major elements + % silt/clay factor. The remaining variation was accounted for by four chemical factors - a PCB subset, lindane, the organotins and two P AHs. This study indicated that physical factors are primarily responsible for structuring Hudson-Raritan benthic communities, although chemical contamination also plays a role, and that ordination techniques are useful for summarizing these complex environmental data sets.